The prediction of coalbed methane content based on multi-scale random forest integrating geological, logging, and seismic data

Accurate estimation of coalbed methane (CBM) content plays a crucial role in assessing and efficiently exploiting CBM resources. Deep CBM is influenced by multiple controlling factors and complex genetic mechanisms. Currently, machine-learning approaches for CBM content prediction typically rely on either seismic or logging data. As a result, the complex geological conditions of deep coal seam are not fully accounted for. This study proposes an intelligent prediction method for CBM content, which achieves multi-source data fusion through a multi-scale modeling and deep integration framework. The approach first extracts multi-scale sensitive attributes or features relevant to CBM content from geological, logging, and seismic sources. For each dataset of the same scale, adaptive modeling is performed using a Bayesian hyperparameter-optimized random forest (RF) algorithm, which enhances model robustness and prevents overfitting. The prediction results from individual scales are subsequently integrated through the least squares method to construct a multi-scale RF composite model. The proposed method is validated using a field dataset and compare its performance with that of conventional approaches, including single-scale RF and linear regression. The results show that, compared with these baseline methods, the proposed method reduces the mean relative error of CBM content prediction on test wells by 3.01 % and 4.94 %, respectively. This demonstrates that the proposed approach achieves higher accuracy and stronger generalization capability, enabling detailed characterization of the spatial distribution of CBM content.